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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import d2lzh as d2l\n",
    "from mpl_toolkits import mplot3d #三维画图\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "def f(x):\n",
    "    return x * np.cos(np.pi * x)\n",
    "\n",
    "x = np.arange(-1.0,2.0,0.1)\n",
    "fig, = plt.plot(x,f(x))\n",
    "fig.axes.annotate(\"local minimum\",xy = (-0.3,-0.25),xytext=(-0.77,-1.0),arrowprops=dict(arrowstyle=\"->\"))\n",
    "fig.axes.annotate(\"global minimum\",xy = (1.1,-0.95),xytext=(0.6,0.8),arrowprops=dict(arrowstyle=\"->\"))\n",
    "\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"f(x\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 鞍点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0, 0.5, 'f(x)')"
     },
     "metadata": {},
     "execution_count": 2
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "x = np.arange(-2.0,2.0,0.1)\n",
    "fig, = plt.plot(x,x**3)\n",
    "fig.axes.annotate(\"saddle point\",xy=(0,-0.2),xytext=(-0.52,-5.0),arrowprops=dict(arrowstyle=\"->\"))\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"f(x)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "AttributeError",
     "evalue": "'Line3DCollection' object has no property 'restride'",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-d0091d8e0dd9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m111\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mprojection\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"3d\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_wireframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"restride\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"cstride\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"rx\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\pytorch1\\lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py\u001b[0m in \u001b[0;36mplot_wireframe\u001b[1;34m(self, X, Y, Z, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1794\u001b[0m                  for xl, yl, zl in zip(txlines, tylines, tzlines)])\n\u001b[0;32m   1795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1796\u001b[1;33m         \u001b[0mlinec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mart3d\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine3DCollection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1797\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlinec\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1798\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_scale_xyz\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mZ\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhad_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\pytorch1\\lib\\site-packages\\matplotlib\\collections.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, segments, linewidths, colors, antialiaseds, linestyles, offsets, transOffset, norm, cmap, pickradius, zorder, facecolors, **kwargs)\u001b[0m\n\u001b[0;32m   1356\u001b[0m             \u001b[0mpickradius\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpickradius\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1357\u001b[0m             \u001b[0mzorder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mzorder\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1358\u001b[1;33m             **kwargs)\n\u001b[0m\u001b[0;32m   1359\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1360\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_segments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msegments\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\pytorch1\\lib\\site-packages\\matplotlib\\collections.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, edgecolors, facecolors, linewidths, linestyles, capstyle, joinstyle, antialiaseds, offsets, transOffset, norm, cmap, pickradius, hatch, urls, offset_position, zorder, **kwargs)\u001b[0m\n\u001b[0;32m    162\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    163\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_path_effects\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 164\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    165\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_paths\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    166\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\pytorch1\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m   1004\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1005\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1006\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1007\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1008\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\pytorch1\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m   1004\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1005\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1006\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1007\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1008\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\pytorch1\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[1;34m(self, k, v)\u001b[0m\n\u001b[0;32m   1000\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1001\u001b[0m                     raise AttributeError('{!r} object has no property {!r}'\n\u001b[1;32m-> 1002\u001b[1;33m                                          .format(type(self).__name__, k))\n\u001b[0m\u001b[0;32m   1003\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1004\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Line3DCollection' object has no property 'restride'"
     ]
    }
   ],
   "source": [
    "x,y = np.mgrid[-1:1:32j,-1:1:31j]\n",
    "z = x**2 - y**2\n",
    "\n",
    "ax = plt.figure().add_subplot(111,projection=\"3d\")\n",
    "ax.plot_wireframe(x,y,z,**{\"rstride\":2,\"cstride\":2})\n",
    "ax.plot([0],[0],[0],\"rx\")\n",
    "ticks = [-1,0,1]\n",
    "plt.xticks(ticks)\n",
    "plt.yticks(ticks)\n",
    "ax.set_zticks(ticks)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0-final"
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  "orig_nbformat": 2,
  "kernelspec": {
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